Applied Research Intern, Proactive Intelligence & Customer World Models

What you'll work on

Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas:

Customer World Models

Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data.

• Representation learning over long-horizon customer histories
• Event-based foundation models
• Multi-modal customer representations spanning structured, sequential, and graph data
• Memory architectures for long-term customer understanding

Proactive Intelligence

Developing systems that can anticipate customer needs and initiate helpful actions before being asked.

• Opportunity detection and next-best-action systems
• Long-horizon planning and decision-making
• Preference and goal inference
• Learning when intervention creates value versus friction

Agentic Decision Systems

Building agents that reason over customer world models and take actions in real environments.

• Tool use and planning
• Multi-step reasoning over customer state
• Autonomous workflow execution
• Recovery and adaptation under uncertainty

Learning from Feedback Loops

Developing methods that allow intelligence to improve continuously from real-world outcomes.

• Reinforcement learning from customer and product feedback
• Reward modeling and preference learning
• Counterfactual evaluation
• Credit assignment over long decision horizons

Evaluation and Measurement

Building evaluation frameworks that predict real-world performance, trust, and customer value.

• Simulated customer environments
• Longitudinal evaluation
• Decision quality metrics
• Safety and reliability benchmarks

What we're looking for

Required

• Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op.
• Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models.
• Experience conducting independent research and translating ideas into working systems.
• Fluency in Python and experience with PyTorch, JAX, or similar frameworks.
• Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work.

Nice to have

• Experience with large language models and agentic systems.
• Experience with reinforcement learning, reward modeling, or sequential decision-making.
• Experience with representation learning for structured, temporal, or graph data.
• Familiarity with large-scale training and production ML systems.
• Interest in building AI systems that directly affect customer outcomes.

What you'll get

• Direct mentorship from researchers working on the future of proactive intelligence at Block.
• Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources.
• Opportunities to publish and contribute to open-source projects.
• A chance to shape foundational technology that could power the next generation of Block products.
• Exposure to both scientific research and product deployment, with a clear path from idea to impact.